Evaluation of Machine Learning Classifiers for Sentiment Analysis

Year : 2024 | Volume : 11 | Issue : 02 | Page : 141 154
    By

    Navdeep Bohra,

  • Ashish Kumari,

  1. Assistant Professor, Department of Computer Science and Engineering, Maharaja Surajmal Institute of Technology, New Delhi, India
  2. Assistant Professor, Department of Computer Science and Engineering, Maharaja Surajmal Institute of Technology, New Delhi, India

Abstract

Sentiment in social media refers to users’ emotions and opinions through their posts and interactions. Sentiment analysis (SA) refers to relating and classifying the sentiments expressed as engagement and interactions between users. When analyzed, tweets frequently produce a large source of clustered data. These data help determine people’s opinions about a variety of motifs. Thus, this study presents an Automated Machine Learning (ML) Sentiment Analysis Model to detect media sentiment. Due to the inclusion of important data and non-useful characters (often called noise), applying models to them gets tricky. This study uses machine literacy to analyze Twitter sentiment analysis, the sentiment of tweets delivered from the Kaggle Twitter Sentiment Analysis dataset by creating a machine learning channel that employs three different models or categories, i.e., Logistic Regression (LR), Bernoulli Naive Bayes (Bernoulli NB), and Support Vector Machine (SVM) on Python. The accuracy and F1 Scores define to measure how well these classifiers function. In the comparison of the models through the accuracy outcome, Logistic Regression gives the most efficient results with an accuracy of 95.83% followed by SVM at 95.77%, it, in turn, performs better than Bernoulli Naive Bayes at 94.90% accuracy.

Keywords: Sentiment analysis, tweets, Bernoulli Naïve Bayes, logistic regression, support vector machine

[This article belongs to Journal of Artificial Intelligence Research & Advances ]

How to cite this article:
Navdeep Bohra, Ashish Kumari. Evaluation of Machine Learning Classifiers for Sentiment Analysis. Journal of Artificial Intelligence Research & Advances. 2024; 11(02):141-154.
How to cite this URL:
Navdeep Bohra, Ashish Kumari. Evaluation of Machine Learning Classifiers for Sentiment Analysis. Journal of Artificial Intelligence Research & Advances. 2024; 11(02):141-154. Available from: https://journals.stmjournals.com/joaira/article=2024/view=155853


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Regular Issue Subscription Original Research
Volume 11
Issue 02
Received 24/05/2024
Accepted 06/07/2024
Published 10/07/2024
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